以增加社会价值为导向的湖南省分类纳税服务研究
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摘要
射频识别技术(RFID)是20世纪90年代开始兴起的一种自动识别技术,其中的盲信号处理技术是近几年来射频识别技术领域的研究热点。本文主要研究了盲信号分离、盲均衡及盲多用户检测技术。盲信号处理技术是在对源信号和传输通道未知的情况下,仅从接收到的混合信号中提取或恢复出原始信号的一种信号处理方法。盲信号处理技术涉及到人工神经网络、统计信号处理、优化信息理论等领域,近年来受到国内外科研机构的广泛重视。
本文通过分析标签与读写器的通信过程,指出多个标签信号与单个读写器通信时,标签信号之间存在信号混叠的问题。针对RFID系统存在的混合信号难以分离的问题,本文提出了独立分量分析(ICA)算法。为验证ICA算法对混合标签信号的分离的有效性,本文在matlab仿真平台上进行了仿真实验,仿真结果显示经ICA盲信号分离算法分离得出的信号与源信号的相对误差很小,表明ICA算法能成功地实现对混合标签信号的分离。本文还对实验存在的不确定性进行了研究,结论表明信号幅度的不确定性对携带关键信息的分离的标签调制信号的影响是可以接受的。仿真结果对多协议的读写器的研究具有一定的借鉴意义,也可作为一种新的标签碰撞问题的解决方法。
由于标签与读写器之间的通信方式是无接触的,标签与读写器通信时会受到其他标签信号的干扰,因此,RFID系统存在的信道均衡及码间干扰问题和读写器接收信号的多址干扰问题。针对信道的码间干扰问题,本文提出了恒模(CMA)盲均衡算法,并对该算法进行了matlab仿真。由MSE,ISI和误码率曲线图可以看出,算法收敛后的误码率较小,减少了标签间的码间干扰,达到了均衡信道的目的。多址干扰是码分多址技术的一个难题,本文对传统的恒模盲多用户检测算法进行改进,提出了基于LS-CMA盲多用户检测算法的信号处理方法。对这种信号处理方法进行matlab仿真及误码率的分析,结果表明这种改进型的信号处理方法较传统的信号处理方法收敛的速度更快,收敛曲线更平稳,误码率更低,具有更强的实用性。
Radio Frequency Identification (RFID) is a kind of automatic identification technology at the beginning of 1990s. Blind Signal Processing technology which belongs to RFID is a research hotspot in recent years. Blind signal separation, blind equalization and blind multiuser detection are mainly studied in this paper. Blind Signal Processing technology is a signal processing method which draw or restore the original signal from receiving mixed signal within the situations that the source signal and transmission channels are unknown. Blind Signal Processing technology which involves the artificial neural network, signal processing, optimizing information theory etc earns widespread respect by research institutions in recent years.
By analyzing the communication between tags and readers, the problem that aliasing signals emerge when multi-signals communicate with a single reader is pointed out in this paper. The independent component analysis (ICA) algorithm is put forward in this paper, in order to deal with the problem that the mixed signals are separated difficultly. To verify the ICA algorithm availably, simulations based on matlab simulation platform are made in this paper. Simulation results show that the relative error between the source signal and the signal which is separated through ICA algorithm. It indicates that the ICA algorithm is effective. The uncertainty in this simulation is studied as well. The conclusion indicates that the influence on tag modulation signal caused by the uncertainty of signal amplitude is acceptable. Simulation results of the research have certain reference value to the research of the reader with multi-protocol. It can also be used as a new method to solve the problem of tag collision.
As the communication between the tag and reader is non-contact, the tag is interfered by other tags when it communicates with the reader. Therefore, channel equalization, inter-symbol interference and the multiple access interference of the reader’s signal exit in the RFID system. The constant modulus blind equalization algorithm (CMA) is suggested in this paper to deal with inter-symbol interference. From the curve of MSE, ISI and the error rate, it can be seen that bit error rate and inter-symbol interference between the tags is reduced after the convergence of the algorithm. The aim of channels equalization is achieved as well. The multiple access interference is one of technical difficulties of multiple access technology. The traditional constant modulus algorithm for blind multiuser detection is improved and a new signal processing methods based on LS-CMA blind multiuser detection is put forward in this paper. By analyzing the matlab simulation and BER, the result shows that compared with the traditional signal processing method, the improved one has more excellent performance which include convergence speed, bit error rate and practicality.
本文通过分析标签与读写器的通信过程,指出多个标签信号与单个读写器通信时,标签信号之间存在信号混叠的问题。针对RFID系统存在的混合信号难以分离的问题,本文提出了独立分量分析(ICA)算法。为验证ICA算法对混合标签信号的分离的有效性,本文在matlab仿真平台上进行了仿真实验,仿真结果显示经ICA盲信号分离算法分离得出的信号与源信号的相对误差很小,表明ICA算法能成功地实现对混合标签信号的分离。本文还对实验存在的不确定性进行了研究,结论表明信号幅度的不确定性对携带关键信息的分离的标签调制信号的影响是可以接受的。仿真结果对多协议的读写器的研究具有一定的借鉴意义,也可作为一种新的标签碰撞问题的解决方法。
由于标签与读写器之间的通信方式是无接触的,标签与读写器通信时会受到其他标签信号的干扰,因此,RFID系统存在的信道均衡及码间干扰问题和读写器接收信号的多址干扰问题。针对信道的码间干扰问题,本文提出了恒模(CMA)盲均衡算法,并对该算法进行了matlab仿真。由MSE,ISI和误码率曲线图可以看出,算法收敛后的误码率较小,减少了标签间的码间干扰,达到了均衡信道的目的。多址干扰是码分多址技术的一个难题,本文对传统的恒模盲多用户检测算法进行改进,提出了基于LS-CMA盲多用户检测算法的信号处理方法。对这种信号处理方法进行matlab仿真及误码率的分析,结果表明这种改进型的信号处理方法较传统的信号处理方法收敛的速度更快,收敛曲线更平稳,误码率更低,具有更强的实用性。
Radio Frequency Identification (RFID) is a kind of automatic identification technology at the beginning of 1990s. Blind Signal Processing technology which belongs to RFID is a research hotspot in recent years. Blind signal separation, blind equalization and blind multiuser detection are mainly studied in this paper. Blind Signal Processing technology is a signal processing method which draw or restore the original signal from receiving mixed signal within the situations that the source signal and transmission channels are unknown. Blind Signal Processing technology which involves the artificial neural network, signal processing, optimizing information theory etc earns widespread respect by research institutions in recent years.
By analyzing the communication between tags and readers, the problem that aliasing signals emerge when multi-signals communicate with a single reader is pointed out in this paper. The independent component analysis (ICA) algorithm is put forward in this paper, in order to deal with the problem that the mixed signals are separated difficultly. To verify the ICA algorithm availably, simulations based on matlab simulation platform are made in this paper. Simulation results show that the relative error between the source signal and the signal which is separated through ICA algorithm. It indicates that the ICA algorithm is effective. The uncertainty in this simulation is studied as well. The conclusion indicates that the influence on tag modulation signal caused by the uncertainty of signal amplitude is acceptable. Simulation results of the research have certain reference value to the research of the reader with multi-protocol. It can also be used as a new method to solve the problem of tag collision.
As the communication between the tag and reader is non-contact, the tag is interfered by other tags when it communicates with the reader. Therefore, channel equalization, inter-symbol interference and the multiple access interference of the reader’s signal exit in the RFID system. The constant modulus blind equalization algorithm (CMA) is suggested in this paper to deal with inter-symbol interference. From the curve of MSE, ISI and the error rate, it can be seen that bit error rate and inter-symbol interference between the tags is reduced after the convergence of the algorithm. The aim of channels equalization is achieved as well. The multiple access interference is one of technical difficulties of multiple access technology. The traditional constant modulus algorithm for blind multiuser detection is improved and a new signal processing methods based on LS-CMA blind multiuser detection is put forward in this paper. By analyzing the matlab simulation and BER, the result shows that compared with the traditional signal processing method, the improved one has more excellent performance which include convergence speed, bit error rate and practicality.
引文
[1]中华人民共和国科学技术部等十五部委.中国射频识别(RFID)技术政策白皮书.北京:中华人民共和国科学技术部, 2006, 2-5
[2]崔荣涛.单通道数字通信信号盲分离技术研究: [中国科学技术大学硕士学位论文].合肥:中国科学技术大学, 2008, 1-21
[3] Fan Wenjie, Xu Xiru. A method for blind separation of components information from mixed pixel. Progress in Natural Science, 2006, 16(7): 760-765
[4]周晓光,王晓华,王伟.射频识别系统设计、仿真与应用.北京:人民邮电出版社, 2008, 1-50
[5]康东,石喜勤,李勇鹏等.射频识别核心技术与典型应用开发案例.北京:人民邮电出版社, 2008, 10-60
[6]谭民,刘禹,曾隽芳等. RFID技术系统工程及应用指南.北京:机械工业出版社, 2007, 5-28
[7]慈新新,王苏滨,王硕.无线射频识别系统技术与应用.北京:人民邮电出版社, 2007, 2-19
[8]杨宇.基于小波分析的RFID射频信号处理: [湖南大学硕士学位论文].长沙:湖南大学, 2008, 1-20
[9]杨行峻,郑君里.人工神经网络与盲信号处理.北京:清华大学出版社, 2002, 327-369
[10] Lang Weimin, Yang Zongkai, Cheng Wenqing, et al. A new ID-based proxy blind signature scheme. Wuhan University Journal of Natural Sciences, 2005, 10(3): 555-558
[11] Liu Yang, Zhang Ming. Blind Source Separation Based on FastICA. In: 2009 Ninth International Conference on Hybrid Intelligent Systems. LiaoNing, 2009, 475-479
[12]杨帆.基于独立分量分析的低压电力线信号去噪的研究: [中南大学硕士学位论文].长沙:中南大学, 2008, 16-21
[13] Ruey wen Liu. Blind signal processing: I-fundamental concepts. Journal of Circuits and Systems, 1996, 1(1): 1-5
[14]张玲.无线通信中的恒模类盲均衡算法研究.通信技术, 2007, 40(12): 72-76
[15]张发启,张斌,张喜斌.盲信号处理及应用.西安:西安电子科技大学出版社, 2006, 1-150
[16]鲁瑞.基于神经网络盲均衡算法的研究: [太原理工大学硕士学位论文].太原:太原理工大学, 2003, 1-30
[17]张立毅,张雄,王华奎等.基于Bussgang技术的盲均衡算法分析.计算机工程与应用, 2003, 74-76
[18]高春圣.基于离散Hopfield神经网络盲均衡新算法的研究: [南京邮电大学硕士学位论文].南京:南京邮电大学, 2006, 1-12
[19]马建仓,牛奕龙,陈海洋.盲信号处理.北京:国防工业出版社, 2006, 85-202
[20] Xiaodong Chai, Chengpeng Zhou, Zhaoyan Feng, et al. Complex-wave retrieval based on blind signal separation. Chinese Optics Letters, 2006, 4(1): 11-14
[21] Li Ning, Shi Tielin. Estimation of the number of correlated sources with common frequencies based on power spectral density. Chinese Journal of Mechanical Engineering, 2007, 20(4): 88-93
[22] Huang Bin. Blind equalization and identification of communication channels: [dissertation]. Alabama: Auburn University, 1999, 63-68
[23] Ruey wen Liu. BSP: II-Blind signals separation. Journal of Circuits and Systems, 1996, 1(2): 1-5
[24]王海林.盲源分离算法研究: [南京邮电大学硕士学位论文].南京:南京邮电大学, 2008, 16-27
[25] Amari S, Chen T P, Cichoki A. Stability analysis of adaptive blind source separation. Neural Network, 1997, 10(8): 1345-1351
[26] Moreau E, Moreau N T. Nonsymmetric contrasts for source separation. IEEE Trans Signal Processing, 1999, 47(8): 2241-2252
[27] Chen Xiaofeng, Zhang Fangguo. ID-based group-oriented proxy signature and blind signature from pairings. Chinese Journal of Electronics, 2006, 15(1): 12-16
[28]谭洪舟,郭雷勇,胡建国.基于盲信号处理的RFID系统反碰撞方法.中国专利. 101281583A, 2008-10-08
[29] Xie Shengli, Tan Beihai, Fu Yuli. Blind signal separation of underdetermined mixtures based on clustering algorithms on planes. Processing in Natural Science, 2007, 17(6): 670-674
[30] Raymond H. A variable step size LMS algorithm. IEEE Trans Signal Processing, 1992, 40: 1633-1642
[31] Godard D. Self-recovering equalization and carrier tracking in two-dimensional data communication system. IEEE Trans Communication, 1980, 28: 1867-1875
[32]冯祥,李建东.调制识别算法及性能分析.电波科学学报, 2005, 20(6): 737-740
[33]王琦.基于独立分量分析的故障源识别技术: [华北电力大学硕士学位论文].北京:华北电力大学, 2008, 19-22
[34]赵曦,张有光.一种新颖的RFID多标签防碰撞算法.北京航空航天大学学报, 2008, 34(3): 276-279
[35] Hongjie Liu, Boqin Feng, Hongming Zheng. The research of blind signal separation algorithm based on natural network. In: Third International Conference on Natural Computation. Haikou, 2007
[36]张立毅,白煜,刘婷等.射频识别中盲均衡算法的研究.计算机工程与设计, 2009, 30(7): 1732-1734
[37] Daniel W, E Schobben, Piet C, W Sommen. A frequency domain blind signal separation method based on decorrelation. IEEE Trans Signal Processing, 2002, 50(8): 1855-1865
[38] Common P. Independent component analysis: a new concept. IEEE Trans Signal Processing, 1994, 36(3): 287
[39]刘勤让,罗小武.信号盲均衡仿真的SIMULINK实现.系统分析与设计, 2000, 30(5): 48-49
[40]赵菊敏,李灯熬,李鸿燕等.基于均衡器输出信号功率的盲均衡算法.计算机工程, 2007, 33(24): 261-265
[41]姚春峰,陈健,阔永红.几种盲均衡算法的原理与仿真试验研究.电子科技, 2007, 212(5): 30-37
[42]赵谦,曾召华. Bussgang盲均衡器.通信技术, 2009, 42(5): 57-62
[43] Cardoso J F, Laheld B H. Equivariant adaptive sourcese parathion. IEEE Trans Signal Processing, 1996, 44(10): 3017
[44] Monsen P. Feedback equalization for fading dispersive channels. IEEE Trans Information Theory, 1971, 56-64
[45] Peng Bing, Yang Zongkai, Wu Shizhong. Research on the validity duration of off-line E-cash. Wuhan University Journal of Natural Sciences, 2004, 9(6): 913-917
[46] Verdu S. Minimum Probability of error for asynchronous Gaussian multiple-acess channels. IEEE Trans Information Theory, 1986, 85-96
[47]张旭秀,王培,邱天爽.基于改进信息最大化法的盲多用户检测.信息与控制, 2008, 37(5): 556-564
[48]杨家轩.独立分量分析及其在多用户检测中的应用: [大连海事大学博士学位论文].大连:大连海事大学, 2008, 25-29
[49]关礼安,杨凯,刘颖等. DS-CDMA系统中基于不同用户数的抗多址干扰性能分析.弹箭与制导学报, 2005, 25(4): 216-218
[50]韩钢.自适应单载波、多载波调制中信号盲检测技术研究: [西安电子科技大学博士学位论文].西安:西安电子科技大学, 2003, 1-16
[51] Zheng F C. Cumulant based deconvolution and identification: several new families of linear equalizers. IEEE Trans Signal Processing, 1993, 30: 199-219
[52] Zhang X D, Wei W. Blind adaptive multiuser detection based on Kalman filtering. IEEE Trans Signal Processing, 2002, 50: 87-95
[53] Kundur D, Hatzinakos D. Blind image deconvolution. IEEE Trans Signal Processing, 1996, 43-64
[2]崔荣涛.单通道数字通信信号盲分离技术研究: [中国科学技术大学硕士学位论文].合肥:中国科学技术大学, 2008, 1-21
[3] Fan Wenjie, Xu Xiru. A method for blind separation of components information from mixed pixel. Progress in Natural Science, 2006, 16(7): 760-765
[4]周晓光,王晓华,王伟.射频识别系统设计、仿真与应用.北京:人民邮电出版社, 2008, 1-50
[5]康东,石喜勤,李勇鹏等.射频识别核心技术与典型应用开发案例.北京:人民邮电出版社, 2008, 10-60
[6]谭民,刘禹,曾隽芳等. RFID技术系统工程及应用指南.北京:机械工业出版社, 2007, 5-28
[7]慈新新,王苏滨,王硕.无线射频识别系统技术与应用.北京:人民邮电出版社, 2007, 2-19
[8]杨宇.基于小波分析的RFID射频信号处理: [湖南大学硕士学位论文].长沙:湖南大学, 2008, 1-20
[9]杨行峻,郑君里.人工神经网络与盲信号处理.北京:清华大学出版社, 2002, 327-369
[10] Lang Weimin, Yang Zongkai, Cheng Wenqing, et al. A new ID-based proxy blind signature scheme. Wuhan University Journal of Natural Sciences, 2005, 10(3): 555-558
[11] Liu Yang, Zhang Ming. Blind Source Separation Based on FastICA. In: 2009 Ninth International Conference on Hybrid Intelligent Systems. LiaoNing, 2009, 475-479
[12]杨帆.基于独立分量分析的低压电力线信号去噪的研究: [中南大学硕士学位论文].长沙:中南大学, 2008, 16-21
[13] Ruey wen Liu. Blind signal processing: I-fundamental concepts. Journal of Circuits and Systems, 1996, 1(1): 1-5
[14]张玲.无线通信中的恒模类盲均衡算法研究.通信技术, 2007, 40(12): 72-76
[15]张发启,张斌,张喜斌.盲信号处理及应用.西安:西安电子科技大学出版社, 2006, 1-150
[16]鲁瑞.基于神经网络盲均衡算法的研究: [太原理工大学硕士学位论文].太原:太原理工大学, 2003, 1-30
[17]张立毅,张雄,王华奎等.基于Bussgang技术的盲均衡算法分析.计算机工程与应用, 2003, 74-76
[18]高春圣.基于离散Hopfield神经网络盲均衡新算法的研究: [南京邮电大学硕士学位论文].南京:南京邮电大学, 2006, 1-12
[19]马建仓,牛奕龙,陈海洋.盲信号处理.北京:国防工业出版社, 2006, 85-202
[20] Xiaodong Chai, Chengpeng Zhou, Zhaoyan Feng, et al. Complex-wave retrieval based on blind signal separation. Chinese Optics Letters, 2006, 4(1): 11-14
[21] Li Ning, Shi Tielin. Estimation of the number of correlated sources with common frequencies based on power spectral density. Chinese Journal of Mechanical Engineering, 2007, 20(4): 88-93
[22] Huang Bin. Blind equalization and identification of communication channels: [dissertation]. Alabama: Auburn University, 1999, 63-68
[23] Ruey wen Liu. BSP: II-Blind signals separation. Journal of Circuits and Systems, 1996, 1(2): 1-5
[24]王海林.盲源分离算法研究: [南京邮电大学硕士学位论文].南京:南京邮电大学, 2008, 16-27
[25] Amari S, Chen T P, Cichoki A. Stability analysis of adaptive blind source separation. Neural Network, 1997, 10(8): 1345-1351
[26] Moreau E, Moreau N T. Nonsymmetric contrasts for source separation. IEEE Trans Signal Processing, 1999, 47(8): 2241-2252
[27] Chen Xiaofeng, Zhang Fangguo. ID-based group-oriented proxy signature and blind signature from pairings. Chinese Journal of Electronics, 2006, 15(1): 12-16
[28]谭洪舟,郭雷勇,胡建国.基于盲信号处理的RFID系统反碰撞方法.中国专利. 101281583A, 2008-10-08
[29] Xie Shengli, Tan Beihai, Fu Yuli. Blind signal separation of underdetermined mixtures based on clustering algorithms on planes. Processing in Natural Science, 2007, 17(6): 670-674
[30] Raymond H. A variable step size LMS algorithm. IEEE Trans Signal Processing, 1992, 40: 1633-1642
[31] Godard D. Self-recovering equalization and carrier tracking in two-dimensional data communication system. IEEE Trans Communication, 1980, 28: 1867-1875
[32]冯祥,李建东.调制识别算法及性能分析.电波科学学报, 2005, 20(6): 737-740
[33]王琦.基于独立分量分析的故障源识别技术: [华北电力大学硕士学位论文].北京:华北电力大学, 2008, 19-22
[34]赵曦,张有光.一种新颖的RFID多标签防碰撞算法.北京航空航天大学学报, 2008, 34(3): 276-279
[35] Hongjie Liu, Boqin Feng, Hongming Zheng. The research of blind signal separation algorithm based on natural network. In: Third International Conference on Natural Computation. Haikou, 2007
[36]张立毅,白煜,刘婷等.射频识别中盲均衡算法的研究.计算机工程与设计, 2009, 30(7): 1732-1734
[37] Daniel W, E Schobben, Piet C, W Sommen. A frequency domain blind signal separation method based on decorrelation. IEEE Trans Signal Processing, 2002, 50(8): 1855-1865
[38] Common P. Independent component analysis: a new concept. IEEE Trans Signal Processing, 1994, 36(3): 287
[39]刘勤让,罗小武.信号盲均衡仿真的SIMULINK实现.系统分析与设计, 2000, 30(5): 48-49
[40]赵菊敏,李灯熬,李鸿燕等.基于均衡器输出信号功率的盲均衡算法.计算机工程, 2007, 33(24): 261-265
[41]姚春峰,陈健,阔永红.几种盲均衡算法的原理与仿真试验研究.电子科技, 2007, 212(5): 30-37
[42]赵谦,曾召华. Bussgang盲均衡器.通信技术, 2009, 42(5): 57-62
[43] Cardoso J F, Laheld B H. Equivariant adaptive sourcese parathion. IEEE Trans Signal Processing, 1996, 44(10): 3017
[44] Monsen P. Feedback equalization for fading dispersive channels. IEEE Trans Information Theory, 1971, 56-64
[45] Peng Bing, Yang Zongkai, Wu Shizhong. Research on the validity duration of off-line E-cash. Wuhan University Journal of Natural Sciences, 2004, 9(6): 913-917
[46] Verdu S. Minimum Probability of error for asynchronous Gaussian multiple-acess channels. IEEE Trans Information Theory, 1986, 85-96
[47]张旭秀,王培,邱天爽.基于改进信息最大化法的盲多用户检测.信息与控制, 2008, 37(5): 556-564
[48]杨家轩.独立分量分析及其在多用户检测中的应用: [大连海事大学博士学位论文].大连:大连海事大学, 2008, 25-29
[49]关礼安,杨凯,刘颖等. DS-CDMA系统中基于不同用户数的抗多址干扰性能分析.弹箭与制导学报, 2005, 25(4): 216-218
[50]韩钢.自适应单载波、多载波调制中信号盲检测技术研究: [西安电子科技大学博士学位论文].西安:西安电子科技大学, 2003, 1-16
[51] Zheng F C. Cumulant based deconvolution and identification: several new families of linear equalizers. IEEE Trans Signal Processing, 1993, 30: 199-219
[52] Zhang X D, Wei W. Blind adaptive multiuser detection based on Kalman filtering. IEEE Trans Signal Processing, 2002, 50: 87-95
[53] Kundur D, Hatzinakos D. Blind image deconvolution. IEEE Trans Signal Processing, 1996, 43-64